Low-voltage generator car non-inductive grid-connected control system and oscillation suppression method

Abstract

The invention relates to a low-voltage generator car non-inductive grid-connected control system and an oscillation suppression method, the system comprises a power grid / low-voltage generator car, a control system and an execution mechanism, the power grid / low-voltage generator car is connected with the control system and is used for communicating with the control system; and the control system is connected with the execution mechanism and is used for controlling non-inductive grid connection of the low-voltage generator car. According to the application, more accurate parameter calculation is realized, compatibility of multiple protocols is avoided, the manual operation time is greatly shortened, and the problems of system oscillation and the like are avoided.

Description

Technical Field

[0001] This application relates to the field of novel power systems, specifically to a sensorless grid-connected control system for a low-voltage generator vehicle and a method for oscillation suppression. Background Technology

[0002] The new power system places higher demands on the rapid access capabilities of distributed emergency power sources. As a core piece of equipment for emergency power supply in the distribution network, the grid connection efficiency and reliability of low-voltage generator trucks directly affect the quality of power supply for residential use. However, the industry has long faced the following pain points: First, fragmented interface protocols. Of the existing generator trucks, 38% use outdated protocols such as Modbus RTU, 45% support the new IEC 61850 standard, and the rest use proprietary protocols, resulting in a protocol parsing failure rate as high as 12.7% during grid connection. Second, grid connection shocks cause system oscillations. Traditional closing relies on manual phase adjustment, with a phase difference tolerance of only ±5°, which easily triggers low-frequency oscillations when the grid frequency fluctuates. Third, lack of multi-vehicle collaborative control. Existing systems cannot achieve wireless parallel operation over 3 kilometers, power distribution relies on physical cables, and emergency response times exceed 15 minutes.

[0003] Existing industry solutions have significant limitations: for example, the piercing access box proposed in State Grid Shandong patent CN202311694075A simplifies physical connections but fails to address protocol compatibility issues; while the dynamic frequency tracking algorithm in China Southern Power Grid Guangzhou patent CN119921389A improves synchronization accuracy, it does not eliminate the risk of oscillations caused by power backflow. With the "Technical Guidelines for Power Emergency Equipment" (GB / T 36549-2023) strengthening requirements for seamless grid connection, developing systems that combine multi-protocol adaptation, zero-impact closing, and active oscillation suppression has become an industry necessity. Summary of the Invention

[0004] The purpose of this application is to provide a seamless grid connection control system and oscillation suppression method for low-voltage generator vehicles. It uses a hardware platform to support long-distance wireless multi-vehicle collaborative control, effectively solving key problems such as inconsistent grid connection protocols of traditional generator vehicles, long manual operation time, and oscillations caused by power backlash. It provides core equipment support for building a new type of power system with fast response, seamless switching, safety and stability, and promotes the advancement of seamless grid connection technology for emergency generator vehicles.

[0005] To achieve the above objectives, this application provides the following technical solution:

[0006] In a first aspect, embodiments of this application provide a seamless grid-connected control system for a low-voltage generator vehicle, including a power grid / low-voltage generator vehicle, a control system, and actuators.

[0007] The power grid / low-voltage generator vehicle is connected to the control system for communication with the control system;

[0008] The control system is connected to the actuator to control the seamless grid connection of the low-voltage generator vehicle.

[0009] The control system includes a Hall current / voltage sensor, a high-speed ADC, an FPGA core controller, a multi-standard physical layer interface, a wireless communication module, an excitation / inverter drive interface, and a hardware direct-connection tripping path interface. The Hall current / voltage sensor collects real-time current and voltage signals from the power grid and converts the analog signals into digital signals via the high-speed ADC before transmitting them to the FPGA core controller. The FPGA core controller is also connected to the multi-standard physical layer interface, which connects to the power grid / low-voltage generator vehicle. The FPGA core controller is also connected to the wireless communication module, the excitation / inverter drive interface, and the hardware direct-connection tripping path interface. The wireless communication module communicates with the power grid / low-voltage generator vehicle. The excitation / inverter drive interface and the hardware direct-connection tripping path interface connect to the actuators to enable the FPGA core controller to control the actuators.

[0010] A method for sensorless grid connection control of a low-voltage generator vehicle includes the following specific steps:

[0011] The protocol is seamlessly compatible, capturing and parsing data from generator vehicles and power grids from multiple heterogeneous protocols, and outputting it as a unified, standardized data stream;

[0012] Microsecond-level grid connection detection continuously and at high speed samples the three-phase current of the power grid, and accurately captures the start time of grid connection or disturbance by calculating the instantaneous rate of change of the current modulus;

[0013] Zero-phase-difference closing control, in the state of waiting to be connected to the grid, generates an excitation regulation signal based on the real-time detected voltage phase difference between the grid and the generator vehicle using the Lyapunov stability control algorithm, drives the phase difference to converge to zero, and performs closing when the preset conditions are met;

[0014] Multi-dimensional oscillation suppression: After the circuit breaker is closed, the system frequency is monitored in real time. Based on the magnitude and duration of the frequency deviation, virtual impedance damping and power feedforward compensation are activated in stages to maintain the stability of the system after grid connection.

[0015] The protocol's seamless compatibility specifically means that...

[0016] The low-voltage generator vehicle is connected via multiple standard physical layer interfaces, and the FPGA core controller captures the underlying bit stream / frame of the corresponding protocol through internal logic.

[0017] Key information for different protocols is located at different offset positions in the message, and the mask is set according to the preset or dynamically issued configuration;

[0018] The mask and data stream are subjected to a bit-level AND operation to extract key fields and core information used to identify the protocol identity.

[0019] The function code extracted from the first level and the key field of the device address are concatenated and fed into a dedicated hash operation unit as input.

[0020] The hash operation unit is implemented in the FPGA core controller through hardware logic. A hardware-optimized variant of the FNV-1a algorithm is used to generate a fixed-length hash value for the combination of key fields. This hash value is the fingerprint of the protocol.

[0021] The FPGA core controller integrates a pre-compiled protocol fingerprint-parser address mapping table. The mapping table is stored in Block RAM and organized in a balanced binary tree or content-addressable memory structure. The generated protocol fingerprint is used as a key for retrieval in this table.

[0022] The retrieval result is a pointer or ID pointing to the corresponding complete protocol parser in memory. Based on this pointer or ID, the parser is dynamically loaded and bound.

[0023] The bound parser is then responsible for parsing the complete raw message into a standardized internal data object.

[0024] Microsecond-level grid connection detection specifically refers to,

[0025] Real-time current signals from the power grid are acquired using a closed-loop Hall effect current sensor, and then conditioned by a front-end conditioning circuit to convert the three-phase currents... The Clarke transformation converts the stationary abc coordinate system to a stationary two-phase system. , Coordinate system;

[0026] Separate the spatial vector information of the current and zero-order components The current modulus is calculated.

[0027] Calculate the current modulus The rate of change over time, dI / dt,

[0028] The mean and variance of the recent current modulus are continuously calculated using a moving average filter to assess the current noise level.

[0029] (1),

[0030] (2),

[0031] To eliminate interference from individual glitch pulses, a valid grid-connected event must satisfy the condition that the dI / dt values ​​at N consecutive sampling points all exceed the dynamic threshold. An improved multi-point summation filter is used to eliminate high-frequency noise in the sampling circuit.

[0032] (3),

[0033] in The average modulus is N, the size of the filter window is N, and k represents the current sampling time. A dynamic threshold is set, and a detection signal is sent when the rate of change of the current modulus is greater than or equal to the threshold.

[0034] The zero-phase-difference closing control is specifically as follows:

[0035] A real-time dynamic model of the phase difference between the power grid and the generator is established based on event-triggered signals.

[0036] Real-time phase difference:

[0037] (4),

[0038] in: The phase angle of the grid voltage is obtained through a 100kHz high-speed sampling circuit. The phase angle of the generator output voltage is adjusted by the excitation controller;

[0039] Constructing the Lyapunov energy function:

[0040] Design a quadratic energy function It needs to meet the following requirements:

[0041] 1. 2. ,

[0042] (5),

[0043] First item The kinetic energy corresponding to the phase deviation, i.e., dynamically adjusting the excitation voltage to correct the rotor magnetic field, the second term The potential energy corresponding to the accumulated phase error, i.e., the integral term eliminating the steady-state error,

[0044] Differentiation of Lyapunov functions:

[0045] (6),

[0046] Introducing a control objective, by adjusting the excitation voltage make Substituting into the derivative:

[0047] (7),

[0048] To meet Eliminate cross-influence, and make

[0049] (8),

[0050] in For proportional gain fast response phase change, To eliminate steady-state error and the influence of cross terms in the integral gain;

[0051] The governing equations are:

[0052] (9),

[0053] when When the threshold is reached, Increasing the excitation current accelerates the generator; when Reduce the excitation current to slow down, so that It continuously shrinks towards the threshold until the closing command is met.

[0054] Multidimensional oscillation suppression specifically refers to,

[0055] When the absolute value of the frequency deviation exceeds the threshold, the virtual impedance layer is activated first. By injecting a preset impedance at the grid connection point to change the network damping characteristics, the low-frequency oscillation energy is specifically absorbed. This is achieved by modifying the voltage command reference value of the inverter PWM controller in real time, thereby effectively changing the output impedance characteristics of the grid connection point at the electrical level.

[0056] Feedforward power compensation P comp The calculation result is used as a direct power regulation quantity and added to the active power reference setpoint of the generator power control loop, thereby directly offsetting the power fluctuations caused by frequency disturbances from the power source.

[0057] Compared with the prior art, the beneficial effects of the present invention are:

[0058] By innovating a multi-protocol adaptive interface, it enables plug-and-play functionality for both new and old equipment. Utilizing high-speed three-phase current sampling and a Lyapunov stability algorithm, it achieves microsecond-level grid connection detection and zero-phase-difference closing control. Furthermore, it employs a triple protection mechanism, including virtual impedance injection and power feedforward compensation, to significantly shorten oscillation suppression time. Its dedicated hardware platform supports long-distance wireless multi-vehicle collaborative control, effectively solving key problems such as inconsistent grid connection protocols in traditional generator vehicles, time-consuming manual operations, and oscillations caused by power backlash. This provides core equipment support for building a new type of power system that is fast-responding, seamlessly switching, and safe and stable, while also promoting the advancement of seamless grid connection technology for emergency generator vehicles. Attached Figure Description

[0059] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0060] Figure 1 This is a system block diagram of this application.

[0061] Figure 2 This is a system diagram of an embodiment of this application.

[0062] Figure 3 A flowchart illustrating the protocol compatibility implementation of this application.

[0063] Figure 4 This is a flowchart of the microsecond-level grid connection testing process in this application.

[0064] Figure 5 This is a flowchart of the zero-phase-difference closing control of this application.

[0065] Figure 6 This is a flowchart of the modular integration system of this application. Detailed Implementation

[0066] The technical solutions of the embodiments of this application will now be described with reference to the accompanying drawings. It should be noted that similar reference numerals and letters in the following drawings indicate similar items; therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0067] The terms “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0068] The terms “first,” “second,” etc., are used only to distinguish one entity or operation from another, and should not be construed as indicating or implying relative importance, nor as requiring or implying any such actual relationship or order between these entities or operations.

[0069] like Figure 1 and Figure 2As shown, a seamless grid-connected control system for a low-voltage generator vehicle includes a power grid / low-voltage generator vehicle, a control system, and actuators.

[0070] The power grid / low-voltage generator vehicle 1 is connected to the control system for communication with the control system;

[0071] The control system is connected to the actuator 9 to control the seamless grid connection of the low-voltage generator vehicle.

[0072] The control system includes a Hall current / voltage sensor 2, a high-speed ADC 3, an FPGA core controller 4, a multi-standard physical layer interface 5, a wireless communication module 6, an excitation / inverter drive interface 7, and a hardware direct-connection tripping path interface 8. The Hall current / voltage sensor 2 collects real-time current and voltage signals from the power grid, and the analog signals are converted into digital signals by the high-speed ADC 3 before being transmitted to the FPGA core controller 4. The FPGA core controller 4 is also connected to the multi-standard physical layer interface 5, which is connected to the power grid / low-voltage generator vehicle 1. The FPGA core controller 4 is also connected to the wireless communication module 6, the excitation / inverter drive interface 7, and the hardware direct-connection tripping path interface 8. The wireless communication module 6 communicates with the power grid / low-voltage generator vehicle 1. The excitation / inverter drive interface 7 and the hardware direct-connection tripping path interface 8 are connected to the actuator 9 to enable the FPGA core controller 4 to control the actuator 9.

[0073] Step 1: Seamless Protocol Compatibility

[0074] like Figure 3As shown, step 1 addresses the challenge of fragmented generator vehicle protocols by employing a hardware-accelerated multi-standard physical layer interface and a three-stage pipeline mechanism. First, physical layer signal capture and key field hardware stripping are performed. A multi-standard physical layer interface compatible with RS-485, CAN, Ethernet, etc., is constructed using a Field-Programmable Gate Array (FPGA) and a set of pluggable or onboard physical layer transceivers (PHYs). The FPGA connects to these PHYs through its flexible I / O pins and uses internal logic to capture the underlying bitstream / frames of the corresponding protocols, such as NRZ encoding / decoding, Manchester encoding / decoding, and CAN bit stuffing / destuffing. Key information for different protocols is located at different offsets in the message. The system can set a mask according to preset or dynamically issued configurations. When the data stream enters the FPGA, this mask is bitwise ANDed with the data stream, retaining only the necessary key fields while masking all other data, accurately extracting the core information used to identify the protocol identity within nanoseconds. Next comes protocol fingerprint generation and efficient matching. The function code and device address key fields extracted in the first stage are concatenated and fed as input into a dedicated hash operation unit (Hash Core). This unit is implemented in hardware logic within the FPGA, employing a hardware-optimized non-encrypted hash algorithm. This invention preferably uses a hardware-optimized variant of the FNV-1a algorithm. This algorithm is chosen because it has excellent avalanche effect and low collision rate, and the calculation process does not involve complex multiplication or table lookup operations. It is easily and efficiently implemented in the FPGA through shift and XOR logic, thus ensuring that the protocol fingerprint generation delay is at the nanosecond level. A fixed-length hash value is generated for this key field combination, and this hash value is the protocol fingerprint. Finally, the parser dynamically binds and standardizes the data stream output. A pre-compiled protocol fingerprint-parser address mapping table is integrated inside the FPGA. This table can be efficiently stored in Block RAM and organized in a balanced binary tree or Content Addressable Memory (CAM) structure. The protocol fingerprint generated in the second stage is used as a keyword for retrieval in this table. The retrieval operation is completed in hardware, with a time complexity of O(log n) or O(1). The retrieval result is a pointer or ID pointing to the corresponding complete protocol parser (a microcode or software module) in memory. The system controller dynamically loads and binds this parser based on this ID. The bound parser is then responsible for parsing the complete raw message into a standardized internal data object. For example, whether the raw message is a Modbus 0x03 register read instruction or an MMS Read service, it will ultimately be converted into a standardized internal structure like {device_id: X, variable: "VA", value:220.1}. Simultaneously, the modular design supports plug-and-play deployment, laying a stable data foundation for subsequent microsecond-level grid connection detection.

[0075] Step 2: Microsecond-level grid connection detection

[0076] like Figure 4 As shown, step 1 eliminates protocol parsing delay and outputs a standardized data stream, avoiding detection failure in step 2 due to data format errors. To perform real-time and accurate calculation of the three-phase current, we must transform it from a complex three-phase time-varying coordinate system (abc) to a static orthogonal coordinate system (α-β) that is easier for microprocessor analysis and control. Step 2 acquires the real-time current signal from the power grid using a closed-loop Hall effect current sensor, and after conditioning by the front-end conditioning circuit, converts the three-phase current... The Clarke transformation converts the stationary abc coordinate system to a stationary two-phase system. , Coordinate system. The key to this transformation is its ability to extract the spatial vector information of the current. and zero-order components As shown in Equation 1, the current modulus is calculated using Equation 2. The system does not use the traditional fixed amplitude threshold, but instead calculates the current modulus. The time rate of change (dI / dt) is calculated. Simultaneously, the mean and variance of the recent current modulus are continuously calculated using a moving average filter to assess the current noise level.

[0077] (1),

[0078] (2),

[0079] To eliminate interference from single glitch pulses, a valid grid-connected event must satisfy the condition that the dI / dt values ​​at N consecutive sampling points all exceed the dynamic threshold. An improved multi-point summation filter is employed to eliminate high-frequency noise in the sampling circuit.

[0080] (3),

[0081] in Let N be the average modulus, N be the size of the filter window, and k be the current sampling time. A dynamic threshold is set; a detection signal is sent when the rate of change of the current modulus is greater than or equal to the threshold.

[0082] Dynamic differential calculation: Calculate the rate of change of current modulus every 0.01ms.

[0083] Condition 1: Rate of change ≥ threshold

[0084] Condition 2: Three consecutive sampling points satisfy Condition 1

[0085] Output logic: After both conditions are met, a detection signal is sent to the closing controller within 1ms.

[0086] Step 3: Zero-phase-difference closing control

[0087] like Figure 5 Since the grid connection time has been accurately captured, the phase difference, frequency difference, and voltage difference between the generator and the grid must be driven to zero simultaneously in the shortest time and in the most stable way to achieve ideal closing. The core of step 3 lies in solving the inrush current problem caused by insufficient accuracy of traditional manual phase adjustment. Step 2 involves an event trigger signal; real-time high-precision measurement of the phase difference between the grid and generator voltages. The zero-phase-difference closing control technology studied achieves precise phase synchronization through a rigorous mathematical framework. Its core lies in establishing a real-time dynamic model of the phase difference between the power grid and the generator (Equation 4). This model relies on the microsecond-level current modulus detection capability of step 2 to refresh data at high speed, ensuring a Δθ resolution of 0.01°. Based on this, an innovative Lyapunov stability control algorithm (Equations 5-9) is designed: Constructing an energy function, its physical meaning—the first term (proportional term) Using a coefficient of 0.8 for fast response phase deviation, the second term (integral term) The steady-state offset is eliminated by accumulating historical errors using a coefficient of 0.2. The proportionality coefficient is... and integral coefficient These settings are not arbitrary. They are the results of iterative optimization performed in the MATLAB / Simulink simulation environment on typical distribution network parameter models and preset step disturbance signals. The optimization goal is to achieve the shortest phase difference convergence time and the smallest overshoot while ensuring system stability. This ensures a rapid response to phase deviations, while This is used to eliminate long-standing steady-state errors, and the combination of the two ensures both speed and accuracy of control. By differentiating and substituting into the frequency difference dynamic equation, the control equation (Equation 9) is derived. The controller dynamically adjusts the generator excitation voltage accordingly. The output phase is corrected in real time by changing the rotor magnetic field. When the phase difference converges to ±0.5° and the frequency difference is ≤0.05Hz, this threshold is determined by reverse calculation from GB / T36549-2023. When the above conditions are met, the circuit breaker is triggered to close the circuit and suppress the inrush current to within 5% of the rated current. This process forms a closed-loop logic: high-precision sensing provides real-time Δθ, and the Lyapunov algorithm generates... The instructions achieve exponential synchronization, and strict thresholds ensure shock-free closing.

[0088] Real-time phase difference:

[0089] (4),

[0090] in: The phase angle of the grid voltage (obtained through a 100kHz high-speed sampling circuit). The phase angle of the generator output voltage (adjusted by the excitation controller).

[0091] Constructing the Lyapunov energy function:

[0092] Design a quadratic energy function It needs to meet the following requirements: 1. >0 (positive definiteness), 2. <0 (negative qualitative, ensuring asymptotic stability)

[0093] (5),

[0094] First item Kinetic energy corresponding to phase deviation (dynamically adjusting excitation voltage to correct rotor magnetic field) Second term The potential energy corresponding to the accumulated phase error (the integral term eliminates the steady-state error).

[0095] Differentiation of Lyapunov functions:

[0096] (6),

[0097] Introducing a control objective, by adjusting the excitation voltage make Substituting into the derivative:

[0098] (7),

[0099] To meet <0, eliminate cross-effects, let

[0100] (8),

[0101] in For proportional gain fast response phase change, Eliminate steady-state error for integral gain. Eliminate the influence of cross terms.

[0102] The governing equations are:

[0103] (9),

[0104] when At the threshold, Increasing the excitation current accelerates the generator; when The excitation current is reduced to slow down the circuit, so that Δθ always shrinks toward the threshold until the closing command is met.

[0105] Step 4: Multi-dimensional Oscillation Suppression

[0106] The core of the multi-dimensional oscillation active suppression system lies in constructing a layered, progressive protection system. Its design logic begins with real-time monitoring of the system frequency after grid connection. When the absolute value of the frequency deviation detected in step 3 exceeds a threshold, the system first activates a virtual impedance layer. This layer alters the network damping characteristics by injecting a preset impedance at the grid connection point—this impedance value, optimized through electromagnetic transient simulation, can specifically absorb low-frequency oscillation energy. Specifically, this is achieved by real-time modification of the voltage command reference value of the inverter's PWM (Pulse Width Modulation) controller, thereby effectively changing the output impedance characteristics at the grid connection point at the electrical level. Feedforward power compensation P comp The calculated result is used as a direct power regulation quantity and added to the active power reference setpoint of the generator power control loop, thereby directly offsetting the power fluctuations caused by frequency disturbances at the power source. This dual dynamic suppression forms a closed-loop control: virtual impedance provides fast damping, and power feedforward achieves precise compensation. Together, they compress the traditional suppression time from the 10-second level to the sub-second level.

[0107] As a final protection measure, the system synchronously performs a cumulative risk assessment: this assessment logic is executed by an independent safety monitoring module within the FPGA. Once triggered, it bypasses the main processor and sends a high-priority trip signal directly to the circuit breaker's trip coil via a dedicated hardware I / O path. When frequency fluctuations continuously exceed a set threshold, the system is determined to have entered an unstable critical state, immediately triggering rapid trip protection. This protection employs a hardware direct-connection trip mechanism, physically isolating faulty units to prevent oscillation propagation. The entire suppression process follows a progressive principle of parameter adjustment followed by compensation, and local operation followed by tripping, maximizing grid-connected operation and reducing cascading failures caused by the failure of a single measure in traditional solutions.

[0108] Step 5: Module Integration Application

[0109] like Figure 6 As shown, step 5 integrates the modules from the first four steps into hardware and achieves multi-vehicle collaborative control. The outputs from steps 1-4 are aggregated via a wireless communication network. The protocol compatibility module from step 1 and the oscillation suppression data from step 4 are transmitted wirelessly for multi-vehicle power coordination; the current sampling from step 2 and the control commands from step 3 are embedded in the FPGA hardware. The controller integration begins with the hardware platform construction: the FPGA is used as the core to handle protocol conversion tasks, and hardware acceleration enables dynamic loading of multiple protocols; the three-phase sampling circuit acquires current modulus at a high rate, providing a real-time data foundation for power distribution. The collaborative work of the two chips compresses signal processing latency to the millisecond level.

[0110] To achieve multi-vehicle collaboration over long distances, the system adopts a wireless communication architecture, whose physical layer parameters ensure a bit error rate of less than 10⁻ even in complex electromagnetic environments. 6After the main controller receives the data and feedback signal Si from each generator unit after the oscillation suppression and stabilization process in step 4 via the wireless link, it starts the dynamic power allocation algorithm: first, it calculates the total power demand of the system. The ratio of the total capacity of the generator cars ∑S to the basic allocation is used to generate the basic allocation; then a proportional term is introduced. Real-time compensation for individual differences ensures that allocation errors are strictly controlled within 3%. The multi-vehicle power allocation algorithm employs dynamic allocation logic based on a consensus protocol.

[0111] (10)

[0112] The closed-loop control characteristic of this process is reflected in continuous data interaction—after the generator car executes the power command, its actual output capacity Si is transmitted back to the main controller via a wireless network. When a vehicle's load rate is detected to deviate from the set value by 0.5%, the algorithm immediately triggers incremental adjustment. This forms a negative feedback loop of instruction issuance → execution feedback → error correction.

[0113] Specific implementation examples:

[0114] The multi-standard physical layer interface receives the communication protocol of the low-voltage generator vehicle and transmits it to the FPGA core controller. The controller then transmits the processed standard data to the low-voltage generator vehicle via the wireless communication module.

[0115] Voltage / current sensors collect voltage and current signals from the power grid and low-voltage generator vehicles in real time. A high-speed ADC converts the collected analog signals into digital signals and sends them to the FPGA core controller.

[0116] Phase 1: Seamless Protocol Compatibility and Standardized Access (Step 1)

[0117] Physical connection: The repair personnel connected the output cables of the three generator trucks to their respective grid connection points, and at the same time connected the data interfaces of the control system of this invention to the switch (Ethernet), RS-485 terminal and CAN interface respectively.

[0118] Protocol Adaptive Recognition: After the control system is powered on, its core FPGA module captures the data stream from the power grid interface in real time through multi-standard physical layer transceivers (PHYs).

[0119] Car A: The system detects the MMS message on the Ethernet, performs an AND operation and mask extraction on key fields such as function code and device address through hardware logic, and sends it to the FNV-1a hash operation unit to generate the "fingerprint" of the IEC 61850 protocol within nanoseconds.

[0120] Car B: The system captures serial data frames from the RS-485 bus and extracts key fields to generate the Modbus RTU protocol "fingerprint".

[0121] Car C: Performed the same operation on the data frames on the CAN bus.

[0122] Dynamic loading of the parser and data standardization: The FPGA's internal Block RAM stores a "protocol fingerprint-parser address" mapping table. Based on the generated fingerprint, the system instantly retrieves and dynamically loads the corresponding complete protocol parser (microcode module). Whether it's a complex MMS service or a simple Modbus read / write command, it is ultimately parsed and converted into a unified internal standardized data structure: {device_id: "Hospital_Grid", variable: "Va", value:220.1}. This process takes less than 10 microseconds, completely eliminating protocol incompatibility barriers and providing standardized data input for subsequent control.

[0123] Phase 2: Microsecond-level grid connection detection and zero-phase-difference closing (Steps 2 and 3)

[0124] Precisely capturing grid connection timing: The generator truck prepares to connect to the grid. Its control system samples the three-phase current of the grid at a high speed of 100kHz using Hall sensors. At the instant the grid connection switch closes, the current modulus changes abruptly. The system calculates the rate of change of the current modulus (dI / dt) in real time and combines it with a judgment logic that the current modulus exceeds a dynamic threshold for three consecutive sampling points, accurately identifying the start time of the grid connection event within 1 millisecond and immediately triggering the closing control program.

[0125] Lyapunov algorithm-driven phase synchronization: After the trigger signal is issued, the system measures in real time that the initial phase difference Δθ between the generator and the grid is -12.5°. At this time, the zero-phase-difference closing control algorithm is activated.

[0126] The controller is based on a preset Lyapunov energy function. Dynamically generate excitation voltage V f The adjustment instructions.

[0127] Proportional term (k) p =0.8) rapid response, high torque correction of phase deviation; integral term (k i =0.2) is responsible for eliminating historical accumulated errors and ensuring no steady-state residuals.

[0128] Driven by the algorithm, the phase difference Δθ converges exponentially. Within 300 milliseconds, the phase difference is precisely controlled within ±0.5°, and the frequency difference is less than 0.05Hz.

[0129] Impact-free closing: When all conditions are met, the system automatically issues a closing command. The on-site monitoring oscilloscope shows that the inrush current at the moment of closing is successfully suppressed to within 5% of the rated current, and the grid voltage fluctuation is less than 0.3%, achieving "seamless grid connection" with zero interference to precision equipment.

[0130] Phase 3: Multi-dimensional oscillation suppression and multi-vehicle coordinated control (Steps 4 and 5)

[0131] System stability and oscillation suppression: After grid connection was completed, a large piece of equipment was started, causing the system frequency to drop by 0.2Hz instantly.

[0132] Fast damping: When the control system detects frequency deviation overshoot, it immediately activates the first layer of protection—virtual damping injection. By modifying the inverter PWM voltage reference command in real time, a preset virtual impedance is electrically equivalent, effectively absorbing the low-frequency oscillation energy caused by this disturbance.

[0133] Precise compensation: Simultaneously, a second layer of protection—power feedforward compensation—is activated. The system calculates the compensation power P based on the frequency change rate dΔf / dt. comp It is then directly superimposed on the active power setpoint of the generator car, actively offsetting the disturbance from the power source.

[0134] With the combined effect of the dual mechanisms, the system frequency recovers to stability within sub-seconds (approximately 750 milliseconds), avoiding power oscillations that may last for several seconds or even tens of seconds in traditional solutions.

[0135] Multi-vehicle wireless collaboration and dynamic power allocation:

[0136] The controllers of the three generator cars automatically form an Ad-hoc network via built-in wireless modules. Car A is dynamically elected as the master controller.

[0137] The main controller aggregates real-time load data from each grid connection point and calculates the total power demand P. total The initial power is 550kW. Based on the capacity of each vehicle and the preset weight, the initial power command is issued through the consensus protocol algorithm: vehicle A (300kW) bears the main load, vehicle B (150kW) bears the medium load, and vehicle C (100kW) bears the light load.

[0138] During operation, the IT load in the power supply area of ​​vehicle B decreased in the evening, and the load rate of vehicle B deviated from the set value by more than 0.5%. Its controller transmitted this status information Si back to the main controller via the wireless network. The main controller algorithm immediately triggered an incremental adjustment ΔP. adjust The system recalculates and issues a new power command, smoothly transferring the excess power generation capacity of vehicle B to vehicle A, which has a heavier load. The entire closed-loop negative feedback adjustment process takes less than 100 milliseconds, achieving the optimization of the overall system operating efficiency.

[0139] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A sensorless grid-connected control system for a low-voltage generator vehicle, characterized in that, This includes the power grid / low-voltage generator vehicle, control system, and actuators. The power grid / low-voltage generator vehicle is connected to the control system for communication with the control system; The control system is connected to the actuator to control the seamless grid connection of the low-voltage generator vehicle.

2. The sensorless grid-connected control system for a low-voltage generator vehicle according to claim 1, characterized in that, The control system includes a Hall current / voltage sensor, a high-speed ADC, an FPGA core controller, a multi-standard physical layer interface, a wireless communication module, an excitation / inverter drive interface, and a hardware direct-connection tripping path interface. The Hall current / voltage sensor collects real-time current and voltage signals from the power grid, converts the analog signals into digital signals via the high-speed ADC, and transmits them to the FPGA core controller. The FPGA core controller is also connected to the multi-standard physical layer interface, which connects to the power grid / low-voltage generator vehicle. The FPGA core controller is also connected to the wireless communication module, the excitation / inverter drive interface, and the hardware direct-connection tripping path interface. The wireless communication module communicates with the power grid / low-voltage generator vehicle. The excitation / inverter drive interface and the hardware direct-connection tripping path interface connect to the actuators to enable the FPGA core controller to control the actuators.

3. A method for sensorless grid connection control of a low-voltage generator vehicle, characterized in that, The specific steps include the following: The protocol is seamlessly compatible, capturing and parsing data from generator vehicles and power grids from multiple heterogeneous protocols, and outputting it as a unified, standardized data stream; Microsecond-level grid connection detection continuously and at high speed samples the three-phase current of the power grid, and accurately captures the start time of grid connection or disturbance by calculating the instantaneous rate of change of the current modulus; Zero-phase-difference closing control, in the state of waiting to be connected to the grid, generates an excitation regulation signal based on the real-time detected voltage phase difference between the grid and the generator vehicle using the Lyapunov stability control algorithm, drives the phase difference to converge to zero, and performs closing when the preset conditions are met; Multi-dimensional oscillation suppression: After the circuit breaker is closed, the system frequency is monitored in real time. Based on the magnitude and duration of the frequency deviation, virtual impedance damping and power feedforward compensation are activated in stages to maintain the stability of the system after grid connection.

4. The method for sensorless grid connection control of a low-voltage generator vehicle according to claim 3, characterized in that, The protocol's seamless compatibility specifically means that... The low-voltage generator vehicle is connected via multiple standard physical layer interfaces, and the FPGA core controller captures the underlying bit stream / frame of the corresponding protocol through internal logic. Key information for different protocols is located at different offset positions in the message, and the mask is set according to the preset or dynamically issued configuration; The mask and data stream are subjected to a bit-level AND operation to extract key fields and core information used to identify the protocol identity. The function code extracted from the first level and the key field of the device address are concatenated and fed into a dedicated hash operation unit as input. The hash operation unit is implemented in the FPGA core controller through hardware logic. A hardware-optimized variant of the FNV-1a algorithm is used to generate a fixed-length hash value for the combination of key fields. This hash value is the fingerprint of the protocol. The FPGA core controller integrates a pre-compiled protocol fingerprint-parser address mapping table. The mapping table is stored in Block RAM and organized in a balanced binary tree or content-addressable memory structure. The generated protocol fingerprint is used as a key for retrieval in this table. The retrieval result is a pointer or ID pointing to the corresponding complete protocol parser in memory. Based on this pointer or ID, the parser is dynamically loaded and bound. The bound parser is then responsible for parsing the complete raw message into a standardized internal data object.

5. The method for sensorless grid connection control of a low-voltage generator vehicle according to claim 3, characterized in that, Microsecond-level grid connection detection specifically refers to, Real-time current signals from the power grid are acquired using a closed-loop Hall effect current sensor, and then conditioned by a front-end conditioning circuit to convert the three-phase currents... The Clarke transformation converts the stationary abc coordinate system to a stationary two-phase system. , Coordinate system; Separate the spatial vector information of the current and zero-order components The current modulus is calculated. Calculate the current modulus The rate of change over time, dI / dt, The mean and variance of the recent current modulus are continuously calculated using a moving average filter to assess the current noise level. ， ， To eliminate interference from individual glitch pulses, a valid grid-connected event must satisfy the condition that the dI / dt values ​​at N consecutive sampling points all exceed the dynamic threshold. An improved multi-point summation filter is used to eliminate high-frequency noise in the sampling circuit. ， in The average modulus is N, the size of the filter window is N, and k represents the current sampling time. A dynamic threshold is set, and a detection signal is sent when the rate of change of the current modulus is greater than or equal to the threshold.

6. The method for sensorless grid connection control of a low-voltage generator vehicle according to claim 5, characterized in that, The zero-phase-difference closing control is specifically as follows: A real-time dynamic model of the phase difference between the power grid and the generator is established based on event-triggered signals. Real-time phase difference: ， in: The phase angle of the grid voltage is obtained through a 100kHz high-speed sampling circuit. The phase angle of the generator output voltage is adjusted by the excitation controller; Constructing the Lyapunov energy function: Design a quadratic energy function It needs to meet the following requirements: , , ， First item The kinetic energy corresponding to the phase deviation, i.e., dynamically adjusting the excitation voltage to correct the rotor magnetic field, the second term The potential energy corresponding to the accumulated phase error, i.e., the integral term eliminating the steady-state error, Differentiation of Lyapunov functions: ， Introducing a control objective, by adjusting the excitation voltage make Substituting into the derivative: ， To meet Eliminate cross-influence, and make ， in For proportional gain fast response phase change, To eliminate steady-state error and the influence of cross terms in the integral gain; The governing equations are: ， when When the threshold is reached, Increasing the excitation current accelerates the generator; when Reduce the excitation current to slow down, so that It continuously shrinks towards the threshold until the closing command is met.

7. The method for sensorless grid connection control of a low-voltage generator vehicle according to claim 6, characterized in that, Multidimensional oscillation suppression specifically refers to, When the absolute value of the frequency deviation exceeds the threshold, the virtual impedance layer is activated first. By injecting a preset impedance at the grid connection point to change the network damping characteristics, the low-frequency oscillation energy is specifically absorbed. This is achieved by modifying the voltage command reference value of the inverter PWM controller in real time, thereby effectively changing the output impedance characteristics of the grid connection point at the electrical level. Feedforward power compensation P comp The calculation result is used as a direct power regulation quantity and added to the active power reference setpoint of the generator power control loop, thereby directly offsetting the power fluctuations caused by frequency disturbances from the power source.

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